SoroushVahidi/scidocs huggingface dataset:
LOCI: Fast Outlier Detection Using the Local Correlation Integral
(2003)

Outlier detection is an integral part of data mining and has attracted much attention recently [BKNS00, JTH01, KNT00]. In this paper, we propose a new method for evaluating outlier-ness, which we call theLocal Correlation Integral(LOCI). As with the best previous methods, LOCI is highly effective for detecting outliers and groups of outliers ( a.k.a.micro-clusters). In addition, it offers the following advantages and novelties: (a) It provides an automatic, data-dictated cut-off to determine whether a point is an outlier—in contrast, previous methods force users to pick cut-offs, without any hints as to what cut-off value is best for a given dataset. (b) It can provide a LOCI plot for each point; this plot summarizes a wealth of information about the data in the vicinity of the point, determining clusters, micro-clusters, their diameters and their inter-cluster distances. None of the existing outlier-detection methods can match this feature, because they output only a single number for each point: its outlierness score.(c) Our LOCI method can be computed as quickly as the best previous methods. (d) Moreover, LOCI leads to a practically linear approximate method, aLOCI (for approximate LOCI ), which provides fast highly-accurate outlier detection. To the best of our knowledge, this is the first work to use approximate computations to speed up outlier detection. Experiments on synthetic and real world data sets show that LOCI and aLOCI can automatically detect outliers and micro-clusters, without user-required cut-offs, and that they quickly spot both expected and unexpected outliers.
A Fast Greedy Algorithm for Outlier Mining
(2006)

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our new algorithm has comparable performance with respect to those state-of-the-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.
Outlier detection by active learning
(2006)

Most existing approaches to outlier detection are based on density estimation methods. There are two notable issues with these methods: one is the lack of explanation for outlier flagging decisions, and the other is the relatively high computational requirement. In this paper, we present a novel approach to outlier detection based on classification, in an attempt to address both of these issues. Our approach isbased on two key ideas. First, we present a simple reduction of outlier detection to classification, via a procedure that involves applying classification to a labeled data set containing artificially generated examples that play the role of potential outliers. Once the task has been reduced to classification, we then invoke a selective sampling mechanism based on active learning to the reduced classification problem. We empirically evaluate the proposed approach using a number of data sets, and find that our method is superior to other methods based on the same reduction to classification, but using standard classification methods. We also show that it is competitive to the state-of-the-art outlier detection methods in the literature based on density estimation, while significantly improving the computational complexity and explanatory power.
Transfer metric learning for action similarity using high-level semantics
(2016)

The goal of transfer learning is to exploit previous experiences and knowledge in order to improve learning in a novel domain. This is especially beneficial for the challenging task of learning classifiers that generalize well when only few training examples are available. In such a case, knowledge transfer methods can help to compensate for the lack of data. The performance and robustness against negative transfer of these approaches is influenced by the interdependence between knowledge representation and transfer type. However, this important point is usually neglected in the literature; instead the focus lies on either of the two aspects. In contrast, we study in this work the effect of various high-level semantic knowledge representations on different transfer types in a novel generic transfer metric learning framework. Furthermore, we introduce a hierarchical knowledge representation model based on the embedded structure in the semantic attribute space. The evaluation of the framework on challenging transfer settings in the context of action similarity demonstrates the effectiveness of our approach compared to state-of-the-art. c © 2015 Elsevier Ltd. All rights reserved.
Songle: A Web Service for Active Music Listening Improved by User Contributions
(2011)

This paper describes a public web service for active music listening,Songle, that enriches music listening experiences by using music-understanding technologies based on signal processing. Although various research-level interfaces and technologies have been developed, it has not been easy to get people to use them in everyday life. Songle serves as a showcase to demonstrate how people can benefit from music-understanding technologies by enabling people to experience active music listening interfaces on the web. Songle facilitates deeper understanding of music by visualizing music scene descriptions estimated automatically, such as music structure, hierarchical beat structure, melody line, and chords. When using music-understanding technologies, however, estimation errors are inevitable. Songle therefore features an efficient error correction interface that encourages people to contribute by correcting those errors to improve the web service. We also propose a mechanism of collaborative training for music-understanding technologies, in which corrected errors will be used to improve the music-understanding performance through machine learning techniques. We hope Songle will serve as a research platform where other researchers can exhibit results of their music-understanding technologies to jointly promote the popularization of the field of music information research.
Combining Data Mining and Machine Learning for Effective Fraud Detection *
(2002)

This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rulelearning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)
(2018)

The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.
IccTA: Detecting Inter-Component Privacy Leaks in Android Apps
(2015)

Shake Them All is a popular "Wallpaper" application exceeding millions of downloads on Google Play. At installation, this application is given permission to (1) access the Internet (for updating wallpapers) and (2) use the device microphone (to change background following noise changes). With these permissions, the application could silently record user conversations and upload them remotely. To give more confidence about how Shake Them All actually processes what it records, it is necessary to build a precise analysis tool that tracks the flow of any sensitive data from its source point to any sink, especially if those are in different components.
 Since Android applications may leak private data carelessly or maliciously, we propose IccTA, a static taint analyzer to detect privacy leaks among components in Android applications. IccTA goes beyond state-of-the-art approaches by supporting inter-component detection. By propagating context information among components, IccTA improves the precision of the analysis. IccTA outperforms existing tools on two benchmarks for ICC-leak detectors: DroidBench and ICC-Bench. Moreover, our approach detects 534 ICC leaks in 108 apps from MalGenome and 2,395 ICC leaks in 337 apps in a set of 15,000 Google Play apps.
Water Quality Measurement and Control from Remote Station for Pisiculture Using NI myRIO Beena
(2015)

A Wireless sensor network is designed and implemented to automatically and remotely monitor and control the water quality parameters such as temperature, turbidity, pH and dissolved oxygen required for pisiculture. The measurements of the system which is at base station are communicated to the remote farmer through built-in Wi-Fi of NI-myRIO and displayed on user’s computer screen from where he can monitor and change the settings of the system. An audio output is also produced from speakers of the computer. SMS alert is sent to farmer’s mobile for variations in the monitored parameters of the system at the base station via GSM modem. The programming of the entire system is done using LabVIEW software and NI myRIO which has inbuilt processor, FPGA, Wi-Fi and web servicing capabilities. Web publishing tool of LabVIEW allows internet access to the front panel of the system to any remote user.
Sub-Optimal Multi-Phase Path Planning: A Method for Solving Rubik's Revenge
(2016)

Rubik’s Revenge, a 4× 4× 4 variant of the Rubik’s puzzles, remains to date as an unsolved puzzle. That is to say, we do not have a method or successful categorization to optimally solve every one of its approximately 7.401×10 possible configurations. Rubik’s Cube, Rubik’s Revenge’s predecessor (3×3×3), with its approximately 4.33 × 10 possible configurations, has only recently been completely solved by Rokicki et. al, further finding that any configuration requires no more than 20 moves. [8] With the sheer dimension of Rubik’s Revenge and its total configuration space, a brute-force method of finding all optimal solutions would be in vain. Similar to the methods used by Rokicki et. al on Rubik’s Cube, in this paper we develop a method for solving arbitrary configurations of Rubik’s Revenge in phases, using a combination of a powerful algorithm known as IDA* and a useful definition of distance in the cube space. While time-series results were not successfully gathered, it will be shown that this method far outweighs current human-solving methods and can be used to determine loose upper bounds for the cube space. Discussion will suggest that this method can also be applied to other puzzles with the proper transformations.
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network
(2018)

Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images. Recent developments in deep neural networks such as drop-out, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over-fitting problem arising due to scarcity of data using drop-out regularizer. Images are normalized and bias field corrected in the pre-processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post-processing to remove small false positives around the edges. A two-phase weighted training method is introduced and evaluated using BRATS 2013 and BRATS 2015 datasets, where it improves the performance parameters of state-of-the-art techniques under similar settings.
Detection of non-flat ground surfaces using V-Disparity images
(2009)

Ground plane detection plays an important role in stereo vision based obstacle detection methods. Recently, V-Disparity image has been widely used for ground plane detection. The existing approach based on V-Disparity image can detect flat ground successfully but have difficulty in detecting non-flat ground. In this paper, we discuss the representation of non-flat ground in V-Disparity image, based on which we propose a method to detect non-flat ground using V-Disparity image.
Across the Digital Divide: A Cross-Country Multi-Technology Analysis of the Determinants of IT Penetration
(2005)

This paper studies the country-level digital divide across successive generations of IT, providing detailed insights into the magnitude and changing nature of the divide. We examine a panel of 40 countries from 1985-2001, based on data from three distinct generations of IT: mainframes, personal computers, and the Internet. Using two measures of IT penetration, we conduct an empirical investigation of socio-economic factors driving the digital divide. We find that IT penetration is positively associated with national income for all three technology generations, and the association between penetration and income is stronger for countries with higher levels of IT penetration. We also examine other demographic and economic factors, going beyond income, and find significant differences in the nature of their effects across countries at different stages of IT adoption. Importantly, factors that previously may have been expanding the divide with earlier technologies are narrowing the gap as the Internet becomes the defining technology of the Information Age.
A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer
(2018)

Style transfer aims to transfer arbitrary visual styles to content images. We explore algorithms adapted from two papers that try to solve the problem of style transfer while generalizing on unseen styles or compromised visual quality. Majority of the improvements made focus on optimizing the algorithm for real-time style transfer while adapting to new styles with considerably less resources and constraints. We compare these strategies and compare how they measure up to produce visually appealing images. We explore two approaches to style transfer: neural style transfer with improvements and universal style transfer. We also make a comparison between the different images produced and how they can be qualitatively measured.
NGMN View on 5G Architecture
(2015)

The fifth generation of mobile technology (5G) will address the socio-economic demands and business contexts of 2020 and beyond. Leading operators from around the globe have defined their vision for 5G, considering customer and business contexts as well as potential technologies and migration issues in an initiative set up by the NGMN Alliance. While their concrete vision is described in the NGMN 5G whitepaper, this paper describes the key points, such as 5G design principles, 5G components, network slicing, 5G radio access technology and 5G interfacing options that have implications to the 5G architecture design.
Highly deformable 3-D printed soft robot generating inching and crawling locomotions with variable friction legs
(2013)

Soft and continuum robots have the useful capability of adopting intricate postures and conforming to complex shapes. Furthermore, structures built from soft materials propagate mechanical energy from one part of the body to another, depending on its body shape, boundary condition, stiffness distribution, and so on. This makes the robots capable of producing a large number of force profiles to achieve useful behaviors and functionalities, even using a small number of actuators. Exploiting the soft mechanical property also enables to generate functional frictional forces, which is a key issue in controlling robot locomotion. In this paper, a highly deformable 3-D printed soft robot (PS robot) is presented, which is capable of generating complex, robust gaits on different inclines using a novel variable friction leg design. This design changes the frictional force depending on the robot posture and shape to facilitate robot locomotion. Shape memory alloy (SMA) coils are embedded into the robot in such a way that they act both as structural elements and actuators. This is the first soft robot platform produced by 3-D printing making fabrication simple and fast.
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
(2018)

This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system. Non-autonomy is implemented by skip connections from the block input to each of the unrolled processing stages and allows stability to be enforced so that blocks can be unrolled adaptively to a pattern-dependent processing depth. NAIS-Net induces non-trivial, Lipschitz input-output maps, even for an infinite unroll length. We prove that the network is globally asymptotically stable so that for every initial condition there is exactly one input-dependent equilibrium assuming tanh units, and multiple stable equilibria for ReL units. An efficient implementation that enforces the stability under derived conditions for both fully-connected and convolutional layers is also presented. Experimental results show how NAIS-Net exhibits stability in practice, yielding a significant reduction in generalization gap compared to ResNets.
Digital dentistry: an overview of recent developments for CAD/CAM generated restorations
(2008)

As in many other industries, production stages are increasingly becoming automated in dental technology. As the price of dental laboratory work has become a major factor in treatment planning and therapy, automation could enable more competitive production in high-wage areas like Western Europe and the USA. Advances in computer technology now enable cost-effective production of individual pieces. Dental restorations produced with computer assistance have become more common in recent years. Most dental companies have access to CAD/CAM procedures, either in the dental practice, the dental laboratory or in the form of production centres. The many benefits associated with CAD/CAM generated dental restorations include: the access to new, almost defect-free, industrially prefabricated and controlled materials; an increase in quality and reproducibility and also data storage commensurate with a standardised chain of production; an improvement in precision and planning, as well as an increase in efficiency. As a result of continual developments in computer hardware and software, new methods of production and new treatment concepts are to be expected, which will enable an additional reduction in costs. Dentists, who will be confronted with these techniques in the future, require certain basic knowledge if they are to benefit from these new procedures. This article gives an overview of CAD/CAM-technologies and systems available for dentistry today.
Cognitive perspectives on SLA The Associative-Cognitive CREED *
(2005)

This paper outlines current cognitive perspectives on second language acquisition (SLA). The Associative-Cognitive CREED holds that SLA is governed by the same principles of associative and cognitive learning that underpin the rest of human knowledge. The major principles of the framework are that SLA is Construction-based, Rational, Exemplar-driven, Emergent, and Dialectic. Language learning involves the acquisition of constructions that map linguistic form and function. Competence and performance both emerge from the dynamic system that is the frequency-tuned conspiracy of memorized exemplars of use of these constructions, with competence being the integrated sum of prior usage and performance being its dynamic contextualized activation. The system is rational in that it optimally reflects prior first language (L1) usage. The L1 tunes the ways in which learners attend to language. Learned-attention transfers to L2 and it is this L1 entrenchment that limits the endstate of usage-based SLA. But these limitations can be overcome by recruiting learner consciousness, putting them into a dialectic tension between the conflicting forces of their current stable states of interlanguage and the evidence of explicit form-focused feedback, either linguistic, pragmatic, or metalinguistic, that allows socially scaffolded development. The paper directs the reader to recent review articles in these key areas and weighs the implications of this framework.
Improving Text Classification by Shrinkage in a Hierarchy of Classes
(1998)

When documents are organized in a large number of topic categories, the categories are often arranged in a hierarchy. The U.S. patent database and Yahoo are two examples. This paper shows that the accuracy of a naive Bayes text classi er can be signi cantly improved by taking advantage of a hierarchy of classes. We adopt an established statistical technique called shrinkage that smoothes parameter estimates of a data-sparse child with its parent in order to obtain more robust parameter estimates. The approach is also employed in deleted interpolation, a technique for smoothing n-grams in language modeling for speech recognition. Our method scales well to large data sets, with numerous categories in large hierarchies. Experimental results on three real-world data sets from UseNet, Yahoo, and corporate web pages show improved performance, with a reduction in error up to 29% over the traditional at classi er.